from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 35.0 | 59.740371 |
| daal4py_KNeighborsClassifier | 0.0 | 5.0 | 24.088212 |
| KNeighborsClassifier_kd_tree | 0.0 | 2.0 | 42.730927 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 0.0 | 32.134958 |
| KMeans_tall | 0.0 | 0.0 | 24.719242 |
| daal4py_KMeans_tall | 0.0 | 0.0 | 9.925425 |
| KMeans_short | 0.0 | 0.0 | 3.569772 |
| daal4py_KMeans_short | 0.0 | 0.0 | 1.859428 |
| LogisticRegression | 0.0 | 0.0 | 25.032409 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 5.096089 |
| Ridge | 0.0 | 0.0 | 11.242000 |
| daal4py_Ridge | 0.0 | 0.0 | 2.209464 |
| HistGradientBoostingClassifier | 0.0 | 5.0 | 6.229003 |
| lightgbm | 0.0 | 5.0 | 26.358922 |
| xgboost | 0.0 | 5.0 | 46.914450 |
| catboost | 0.0 | 5.0 | 18.174028 |
| total | 1.0 | 7.0 | 40.130145 |
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
scikit-learn's estimators vs daal4py¶reporting = Reporting(config_file_path="config.yml")
reporting.run()
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.172 | 0.000 | 4.656 | 0.000 | 1 | 100 | NaN | NaN | 0.486 | 0.000 | 0.354 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.624 | 0.268 | 0.000 | 0.025 | 1 | 100 | 0.938 | 0.800 | 3.858 | 0.045 | 6.382 | 0.102 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.201 | 0.004 | 0.000 | 0.201 | 1 | 100 | 1.000 | 1.000 | 0.101 | 0.002 | 1.985 | 0.061 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.125 | 0.000 | 6.382 | 0.000 | -1 | 100 | NaN | NaN | 0.479 | 0.000 | 0.261 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 37.608 | 0.000 | 0.000 | 0.038 | -1 | 100 | 0.938 | 0.800 | 3.825 | 0.018 | 9.832 | 0.047 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.197 | 0.012 | 0.000 | 0.197 | -1 | 100 | 1.000 | 1.000 | 0.102 | 0.002 | 1.941 | 0.118 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.124 | 0.000 | 6.433 | 0.000 | -1 | 5 | NaN | NaN | 0.483 | 0.000 | 0.257 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 37.594 | 0.000 | 0.000 | 0.038 | -1 | 5 | 0.818 | 0.937 | 3.903 | 0.029 | 9.633 | 0.071 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.197 | 0.015 | 0.000 | 0.197 | -1 | 5 | 1.000 | 1.000 | 0.102 | 0.001 | 1.933 | 0.151 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.131 | 0.000 | 6.119 | 0.000 | -1 | 1 | NaN | NaN | 0.469 | 0.000 | 0.279 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 28.683 | 0.301 | 0.000 | 0.029 | -1 | 1 | 0.720 | 0.711 | 3.823 | 0.026 | 7.503 | 0.093 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.181 | 0.016 | 0.000 | 0.181 | -1 | 1 | 0.000 | 1.000 | 0.100 | 0.001 | 1.807 | 0.161 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.121 | 0.000 | 6.637 | 0.000 | 1 | 1 | NaN | NaN | 0.467 | 0.000 | 0.258 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 15.540 | 0.366 | 0.000 | 0.016 | 1 | 1 | 0.720 | 0.937 | 3.925 | 0.035 | 3.959 | 0.100 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.191 | 0.003 | 0.000 | 0.191 | 1 | 1 | 0.000 | 1.000 | 0.102 | 0.002 | 1.872 | 0.052 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.130 | 0.000 | 6.174 | 0.000 | 1 | 5 | NaN | NaN | 0.464 | 0.000 | 0.279 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.398 | 0.298 | 0.000 | 0.024 | 1 | 5 | 0.818 | 0.711 | 3.805 | 0.022 | 6.412 | 0.087 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.201 | 0.005 | 0.000 | 0.201 | 1 | 5 | 1.000 | 1.000 | 0.104 | 0.004 | 1.933 | 0.090 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.298 | 0.000 | 1 | 100 | NaN | NaN | 0.100 | 0.000 | 0.536 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.770 | 0.142 | 0.000 | 0.020 | 1 | 100 | 0.980 | 0.985 | 0.833 | 0.019 | 23.748 | 0.557 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.024 | 0.002 | 0.000 | 0.024 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.001 | 5.163 | 1.023 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.057 | 0.000 | 0.279 | 0.000 | -1 | 100 | NaN | NaN | 0.106 | 0.000 | 0.540 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 32.532 | 0.000 | 0.000 | 0.033 | -1 | 100 | 0.980 | 0.985 | 0.825 | 0.017 | 39.428 | 0.824 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.028 | 0.003 | 0.000 | 0.028 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.001 | 5.870 | 1.428 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.296 | 0.000 | -1 | 5 | NaN | NaN | 0.104 | 0.000 | 0.518 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 32.521 | 0.000 | 0.000 | 0.033 | -1 | 5 | 0.979 | 0.985 | 0.900 | 0.020 | 36.152 | 0.817 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.029 | 0.002 | 0.000 | 0.029 | -1 | 5 | 1.000 | 1.000 | 0.004 | 0.000 | 6.724 | 0.690 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.274 | 0.000 | -1 | 1 | NaN | NaN | 0.095 | 0.000 | 0.613 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 23.625 | 0.127 | 0.000 | 0.024 | -1 | 1 | 0.963 | 0.976 | 0.815 | 0.018 | 28.988 | 0.661 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.001 | 0.000 | 0.020 | -1 | 1 | 1.000 | 1.000 | 0.004 | 0.000 | 4.620 | 0.431 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.285 | 0.000 | 1 | 1 | NaN | NaN | 0.101 | 0.000 | 0.555 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 10.653 | 0.082 | 0.000 | 0.011 | 1 | 1 | 0.963 | 0.985 | 0.885 | 0.020 | 12.038 | 0.287 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.001 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.001 | 3.038 | 0.399 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.057 | 0.000 | 0.282 | 0.000 | 1 | 5 | NaN | NaN | 0.099 | 0.000 | 0.575 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.870 | 0.352 | 0.000 | 0.020 | 1 | 5 | 0.979 | 0.976 | 0.821 | 0.013 | 24.193 | 0.583 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.023 | 0.001 | 0.000 | 0.023 | 1 | 5 | 1.000 | 1.000 | 0.004 | 0.000 | 5.255 | 0.422 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.190 | 0.000 | 0.025 | 0.000 | 1 | 1 | NaN | NaN | 0.791 | 0.000 | 4.033 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.743 | 0.019 | 0.000 | 0.001 | 1 | 1 | 0.963 | 0.967 | 0.121 | 0.007 | 6.140 | 0.410 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 2.685 | 1.216 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.906 | 0.000 | 0.028 | 0.000 | -1 | 1 | NaN | NaN | 0.707 | 0.000 | 4.113 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.433 | 0.006 | 0.000 | 0.000 | -1 | 1 | 0.963 | 0.977 | 0.221 | 0.008 | 1.963 | 0.073 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.002 | 0.000 | 0.004 | -1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 7.560 | 5.148 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.908 | 0.000 | 0.028 | 0.000 | 1 | 100 | NaN | NaN | 0.711 | 0.000 | 4.088 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.973 | 0.134 | 0.000 | 0.005 | 1 | 100 | 0.979 | 0.978 | 0.697 | 0.067 | 7.133 | 0.711 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 2.377 | 0.873 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.145 | 0.000 | 0.025 | 0.000 | -1 | 5 | NaN | NaN | 0.734 | 0.000 | 4.283 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.857 | 0.015 | 0.000 | 0.001 | -1 | 5 | 0.972 | 0.978 | 0.667 | 0.014 | 1.286 | 0.034 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 2.703 | 0.983 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.021 | 0.000 | 0.026 | 0.000 | -1 | 100 | NaN | NaN | 0.712 | 0.000 | 4.244 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.780 | 0.036 | 0.000 | 0.003 | -1 | 100 | 0.979 | 0.977 | 0.218 | 0.006 | 12.725 | 0.378 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 9.976 | 3.649 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.870 | 0.000 | 0.028 | 0.000 | 1 | 5 | NaN | NaN | 0.693 | 0.000 | 4.139 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.443 | 0.022 | 0.000 | 0.001 | 1 | 5 | 0.972 | 0.967 | 0.114 | 0.003 | 12.629 | 0.345 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 2.679 | 1.067 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.752 | 0.000 | 0.021 | 0.000 | 1 | 1 | NaN | NaN | 0.489 | 0.000 | 1.538 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.033 | 0.001 | 0.000 | 0.000 | 1 | 1 | 0.968 | 0.986 | 0.001 | 0.000 | 40.940 | 19.078 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.002 | 2.585 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.829 | 0.000 | 0.019 | 0.000 | -1 | 1 | NaN | NaN | 0.506 | 0.000 | 1.637 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.037 | 0.005 | 0.000 | 0.000 | -1 | 1 | 0.968 | 0.991 | 0.001 | 0.000 | 30.462 | 7.897 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 14.880 | 8.261 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.760 | 0.000 | 0.021 | 0.000 | 1 | 100 | NaN | NaN | 0.481 | 0.000 | 1.581 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.062 | 0.002 | 0.000 | 0.000 | 1 | 100 | 0.984 | 0.988 | 0.009 | 0.003 | 7.251 | 2.233 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.316 | 2.361 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.797 | 0.000 | 0.020 | 0.000 | -1 | 5 | NaN | NaN | 0.480 | 0.000 | 1.660 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.036 | 0.001 | 0.000 | 0.000 | -1 | 5 | 0.980 | 0.988 | 0.007 | 0.001 | 5.037 | 0.635 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 17.438 | 7.653 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.776 | 0.000 | 0.021 | 0.000 | -1 | 100 | NaN | NaN | 0.504 | 0.000 | 1.539 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.057 | 0.002 | 0.000 | 0.000 | -1 | 100 | 0.984 | 0.991 | 0.001 | 0.000 | 51.379 | 12.245 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 24.240 | 12.751 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.798 | 0.000 | 0.020 | 0.000 | 1 | 5 | NaN | NaN | 0.506 | 0.000 | 1.576 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.037 | 0.002 | 0.000 | 0.000 | 1 | 5 | 0.980 | 0.986 | 0.001 | 0.002 | 28.141 | 39.221 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 4.827 | 2.264 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.683 | 0.0 | 0.702 | 0.000 | k-means++ | NaN | 30 | NaN | 0.314 | 0.0 | 2.174 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.0 | 0.273 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.674 | 3.266 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.803 | 3.810 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.603 | 0.0 | 0.796 | 0.000 | random | NaN | 30 | NaN | 0.271 | 0.0 | 2.227 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.0 | 0.317 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 6.751 | 2.837 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.900 | 3.743 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 7.314 | 0.0 | 3.281 | 0.000 | k-means++ | NaN | 30 | NaN | 3.872 | 0.0 | 1.889 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 13.425 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.968 | 1.843 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.0 | 0.016 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 6.133 | 5.768 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.536 | 0.0 | 3.672 | 0.000 | random | NaN | 30 | NaN | 3.701 | 0.0 | 1.766 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 13.763 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 6.021 | 2.099 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.0 | 0.016 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.678 | 4.110 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.301 | 0.000 | 0.011 | 0.000 | k-means++ | NaN | 20 | NaN | 0.051 | 0.0 | 5.895 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.160 | 0.000 | k-means++ | -0.000 | 20 | 0.001 | 0.001 | 0.0 | 3.304 | 0.545 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.000 | 0.000 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.136 | 4.091 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.093 | 0.000 | 0.034 | 0.000 | random | NaN | 20 | NaN | 0.133 | 0.0 | 0.697 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.166 | 0.000 | random | 0.003 | 20 | 0.000 | 0.001 | 0.0 | 3.198 | 0.589 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.001 | 0.000 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.680 | 5.473 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 1.064 | 0.000 | 0.150 | 0.000 | k-means++ | NaN | 20 | NaN | 0.252 | 0.0 | 4.222 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.000 | 5.393 | 0.000 | k-means++ | 0.260 | 20 | 0.364 | 0.001 | 0.0 | 2.070 | 0.266 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.010 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.205 | 3.067 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.318 | 0.000 | 0.503 | 0.000 | random | NaN | 20 | NaN | 0.607 | 0.0 | 0.524 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.000 | 5.557 | 0.000 | random | 0.293 | 20 | 0.290 | 0.001 | 0.0 | 2.088 | 0.213 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.010 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 6.815 | 4.722 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 15.358 | 0.0 | [-0.07682523] | 0.000 | NaN | NaN | NaN | NaN | NaN | 2.709 | 0.000 | 5.670 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [48.5965174] | 0.000 | NaN | NaN | NaN | NaN | 0.543 | 0.000 | 0.000 | 0.844 | 0.336 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.18079014] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.000 | 0.000 | 0.353 | 0.230 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [28] | 1.319 | 0.0 | [-1.55839] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.920 | 0.000 | 1.434 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [28] | 0.002 | 0.0 | [99.47988452] | 0.000 | NaN | NaN | NaN | NaN | 0.230 | 0.004 | 0.001 | 0.539 | 0.080 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [28] | 0.000 | 0.0 | [18.43005547] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.000 | 0.148 | 0.071 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.293 | 0.000 | 0.273 | 0.0 | NaN | NaN | NaN | 0.288 | 0.0 | 1.018 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.012 | 0.001 | 6.845 | 0.0 | NaN | NaN | 0.094 | 0.018 | 0.0 | 0.649 | 0.072 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 0.790 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.615 | 0.396 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.388 | 0.000 | 0.576 | 0.0 | NaN | NaN | NaN | 0.342 | 0.0 | 4.054 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.000 | 4.362 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.715 | 0.335 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.009 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.685 | 0.426 | See | See |
reporting_hpo = ReportingHpo(files=[
"results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
"results/benchmarking/xgboost_XGBClassifier.csv",
"results/benchmarking/lightgbm_LGBMClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier.csv"
])
reporting_hpo.run()